Offering relevant products is becoming increasingly important for e-commerce companies in order for them to effectively attract and retain consumers given the ever increasing number of competitors emerging on the Internet. As consumers are faced with an overwhelming selection of products, content, and/or service online, companies are faced with an equal level of decision complexity in order to effectively determine which of their ever expansive inventory of products should be offered to a consumer population the vast majority, of which are anonymous visitors of their online stores. This lack of visibility into the interests and shopping preferences of a large and often heterogeneous consumer base leads to suboptimal marketing and merchandising strategies as a result of undifferentiated product offerings.
The economic implications of non-relevant product offerings are quite considerable and could determine the long-term viability of present e-commerce companies engaged in a pernicious business cycle as they are forced to spend more on acquiring new customers in order to compensate for their turnover of the consumers that have previously visited and may have purchased within their online stores.
The standard approaches used by e-commerce companies to target customers is based on multivariate analysis, segmentation, and list generation of demographic and psychographic data, preference data provided during account registration online and/or historic purchase data of individual users in standard data-mart/data-warehouse environments. Each of these criteria presents significant limitations in enabling effective and scalable targeting of online consumers. First, demographic and psychographic data offers poor resolution into the nuanced interests of customers to specific products or product classes within a wide array of highly diversified inventories. In addition, only the disproportionately small population of consumers that have provided their identifiable address information (i.e., buyers, registrants/account holders, etc.) can be classified based on these criteria and thus targeted. The vast majority of online shoppers, who are anonymous visitors, simply can not be targeted.
Second, in the case of the use of interest or preference data explicitly provided by online consumers when they register or create accounts, such data is often sparse and unreliable in determining a customer's true shopping interests. It is usually non-reflective on what a particular customer has actually purchased, if they have purchased at all. This is similar in many ways to the demographic and psychographic data which has limited consumer reach and allows for targeting of disproportionately small populations.
Lastly, the third criteria for targeting consumers considered most effective by traditional brick-and-mortar companies and optimized, in particular, by retail catalog companies, is data on historic purchasing activity. While initial purchasing activity is an often effective determinant of future purchasing activity, it is dependent on the type of product being offered and their natural buying cycles (i.e., refrigerators and mortgage packages versus groceries and DVDs, etc.). Such factors determine the likelihood of repeat purchase rates. Response rates often drop precipitously on the second and future campaigns as natural buying thresholds have been exceeded.
Analysis of order data has been the mainstay of current database marketing/business intelligence technologies due in large part to its success in traditional catalog retail business models. When applied to e-commerce, the use of an order-centric data model, as typified in the canonical data warehouse star-schemas, presents significant limitations as an artifact of an old world brick-and-mortar paradigm. With point-of-sale systems such as cash registers as the primary transactional system of record, purchasing activity has been the central event space for analysis by commercial consumer oriented database systems offering a very myopic view of the breadth of important shopping dynamics that are occurring.
Despite the emergence of e-commerce and its vast new sources of transactional data, the capacity of e-commerce companies to effectively segment and target market and merchandise to their customers has remained a considerable challenge. Of the many reasons why efficient use of clickstream data has remained elusive for e-commerce companies, the most noteworthy data management and analytical limitations include: unwieldy volumes (terabytes) of raw transactional data requiring high storage and processing capacity, non-standardized data structures leading to limited semantic resolution and join complexity from modeling of multiple and heterogeneous event spaces due to dimensional non-conformity, and disproportionately small population of known consumers such as buyers and registrants, that are often considered more valuable to companies, whose clickstream data can actually be applied to them and be effectively leveraged to increase revenue and profits.
Many current solutions in the market have developed approaches to integrating voluminous clickstream data but still offer little to no improved ability to effectively target their consumers in order to increase revenues and profits. Many of the packaged data warehouse solutions, while integrating clickstream data, have architected the schema based on traditional approaches that make multivariate analysis across single or the desired multiple events inordinately processing intensive and often improbable to conduct. Given the cost of storing and processing terabytes of raw clickstream data, such packaged solutions are still oriented towards standard order-centric schemas and data architectures.
To fulfill this growing need to store and process terabytes of clickstream data in a cost-effective manner for e-commerce companies, web analytic service companies emerged. Many of these companies serve ostensibly as outsourced data warehouse solutions for e-commerce companies. Their technology services allow for the rapid processing of clickstream data in order to provide reports for aggregate traffic analysis, page performance, site usage, and conversion analysis. Rarely can such patterns give insight into meaningful shopping patterns that can be readily attributed to individual or segments of customers for target marketing and merchandising.
Only recently, and in rare cases, are the client company's internal customer ids provided to such third party analytic services to allow for true onsite behavioral mapping and identification. The emergence of customer-level clickpath aggregation has led to new technologies in partnership with Email Service Providers whereby client companies can set up specific business rules to instantiate automated targeting events. The best known involves the use of trigger-based events where consumers that exhibit specified actions online (i.e., abandon item in shopping cart, download article, etc.) are sent a targeted email relating to the event in order to influence a desired activity such as a purchase or subscription.
Despite major advancements in processing power and storage capacity, most commerce analytic data systems (i.e., data warehouses, data marts, etc.) fail to provide companies with the ability to determine and launch high-performance campaigns by effectively determining what to offer their fickle and largely anonymous mass of customers as well as the means of targeting them in the rare occasions that their interests and preferences are determined. The analytic limitations of current direct marketing and merchandising technology solutions are the result of the continued use of an increasingly outdated commerce data model paradigm, inherent in brick-and-mortar systems, which are primarily designed to mine order-centric activity, albeit across a limitless set of dimensions.
Given the aforementioned, a need exists for decision support/revenue management system that effectively models the full breadth and depth of e-commerce data to enable companies to optimize servicing of their customers based on revenue projections of their differentiated shopping behaviors.